Compare RNNs, LSTMs, Transformers, and MPC

Quick Overview

This question evaluates understanding of sequence-modeling architectures (RNNs, LSTMs, Transformers) and Model Predictive Control, assessing architectural choice, training and optimization trade-offs, handling of long-range dependencies, and methods for integrating learned dynamics with control.

Compare RNNs, LSTMs, Transformers, and MPC

Company: Tesla

Role: Machine Learning Engineer

Category: Machine Learning

Difficulty: hard

Interview Round: Technical Screen

Explain how you used RNNs, LSTMs, and Transformers in your project. Compare their capabilities for sequence modeling, training considerations, and when you would choose each. Specify which Transformer architecture you used (encoder-only, decoder-only, or encoder–decoder) and the reasoning. Briefly explain the fundamentals of Model Predictive Control (MPC)—including cost function, constraints, and control horizon—and discuss how MPC could be combined with learning-based models.

Overview: This question evaluates understanding of sequence-modeling architectures (RNNs, LSTMs, Transformers) and Model Predictive Control, assessing architectural choice, training and optimization trade-offs, handling of long-range dependencies, and methods for integrating learned dynamics with control.

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Tesla
Sep 6, 2025
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Sequence Modeling Architectures and MPC (Technical Screen)

You worked on a sequence-modeling project involving multivariate time-series signals and multi-step prediction/control. Address the following:

  1. Architecture usage
    • Explain how you used RNNs, LSTMs, and Transformers in your project (what the task was, why each model was chosen, and what changed across iterations).
  2. Capability and training comparison
    • Compare RNNs, LSTMs, and Transformers for sequence modeling: handling long-range dependencies, data/computation needs, latency, and robustness.
    • Discuss training considerations: optimization, stability, batching/streaming, masking, teacher forcing vs. autoregression, and regularization.
  3. Transformer design choice
    • Specify which Transformer architecture you used (encoder-only, decoder-only, or encoder–decoder) and why it fit your task.
  4. MPC fundamentals
    • Briefly explain Model Predictive Control (MPC): cost function, constraints, prediction/control horizons, and the receding horizon principle.
  5. Combining MPC with learning
    • Discuss how MPC can be integrated with learning-based models (e.g., learned dynamics, uncertainty handling, imitation/distillation, safety).
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